Predict

parameters = list(beta = 2.47,
                  sigma = 0.192, gamma = 0.434,
                  mu = 0.00005)
initials = c(S = 0.8,
             E = 0.2,
             I = 0,
             R = 0)
seir = SEIR(pars = parameters,init = initials, time = 0:60) # time unit is day
PlotMods(seir)

#seir$results
#seir$model

parameters.2 = list(beta = 2.47*0.5,
                  sigma = 0.192, gamma = 0.434,
                  mu = 0.00005
                  )
initials = c(S = 0.8,
             E = 0.2,
             I = 0,
             R = 0)
seir.2 = SEIR(pars = parameters.2,init = initials, time = 0:60) # time unit is day
PlotMods(seir.2)

use ggplot to draw the plot

#use ggplot to draw the plot
function2 <- function(time, init, pars) {
        with(as.list(c(init, pars)), {
            dS = mu - S * (beta * I + mu)
            dE = beta * S * I - E * (sigma + mu)
            dI = sigma * E - I * (gamma + mu)
            dR = gamma * I - mu * R
            list(c(dS, dE, dI, dR))
        })
}

parameters = list(beta = 2.47,
                  sigma = 0.192, gamma = 0.434,
                  mu = 0.00005)

time = 0:60

initials = c(S = 0.9,
             E = 0.0001,
             I = 0,
             R = 1-0.9-0.0001)

Flat the curve

#falt the curve
output <- ode(times = time, func = function2, y = initials, parms = parameters)  #without social distance
output.2 = ode(times = time, func = function2, y = initials, parms = parameters.2) #with social distance
    
visual_data = as.data.frame(output) %>% 
  mutate(group = 1)
visula_2 = as.data.frame(output.2) %>% 
  mutate(group = 2)
vis = full_join(visual_data,visula_2) %>% 
  mutate(group = as.factor(group))

## Joining, by = c("time", "S", "E", "I", "R", "group")

#I curve when beta is different
fig1 = vis %>% 
  group_by(group) %>% 
  ggplot(aes(x = time, y = I,color = group)) + geom_line()
ggplotly(fig1)

SEIR in one plot

#SEIR in one plot
vis_seir = visual_data %>% 
  mutate(
    p_S = S,
    p_E = E,
    p_I = I,
    p_R = R
  ) %>% 
  select(time,p_S, p_E, p_I, p_R) %>% 
  pivot_longer(
    p_S:p_R,
    names_to = "box", 
    names_prefix = "p_",
    values_to = "proportion")

fig2 = vis_seir %>% 
  ggplot(aes(x = time, y = proportion,group = box,color = box)) + geom_line()

ggplotly(fig2)